Merge branch 'dev0.8.0' of github.com:fastnlp/fastNLP into dev0.8.0

fastNLP/core/collators/padders/get_padder.py

@@ -118,6 +118,7 @@ def get_padder(batch_field:Sequence[Any], pad_val, dtype, backend, field_name)->
             elif backend == 'numpy':
                 return NumpyTensorPadder(pad_val=pad_val, ele_dtype=None, dtype=dtype)
             elif backend == 'torch':
+                # 这里 ele_dtype 传入为 None 的原因是防止出现 paddle tensor 转换为 torch tensor
                 return TorchTensorPadder(pad_val=pad_val, ele_dtype=None, dtype=dtype)
             elif backend == 'paddle':
                 return PaddleTensorPadder(pad_val=pad_val, ele_dtype=None, dtype=dtype)

fastNLP/core/collators/padders/paddle_padder.py

@@ -132,6 +132,9 @@ class PaddleTensorPadder(Padder):
         try:
             if not isinstance(batch_field[0], paddle.Tensor):
                 batch_field = [paddle.to_tensor(field.tolist(), dtype=dtype) for field in batch_field]
+            else:
+                if dtype is None:
+                    dtype = batch_field[0].dtype
         except AttributeError:
             raise RuntimeError(f"If the field is not a paddle.Tensor (it is {type(batch_field[0])}), "
                                f"it must have tolist() method.")

fastNLP/core/collators/padders/torch_padder.py

@@ -118,6 +118,8 @@ class TorchTensorPadder(Padder):
                 batch_field = [torch.tensor(field.tolist(), dtype=dtype) for field in batch_field]
             else:
                 device = batch_field[0].device
+                if dtype is None:
+                    dtype = batch_field[0].dtype
         except AttributeError:
             raise RuntimeError(f"If the field is not a torch.Tensor (it is {type(batch_field[0])}), "
                                f"it must have tolist() method.")

fastNLP/core/controllers/evaluator.py

@@ -169,14 +169,14 @@ class Evaluator:
                 raise e
             finally:
                 self.finally_progress_bar()
-
-        metric_results = flat_nest_dict(metric_results, separator=self.separator, compress_none_key=True, top_down=False)
+        if len(metric_results) > 0:  # 如果 metric 不为 None 需要 print 。
+            metric_results = flat_nest_dict(metric_results, separator=self.separator, compress_none_key=True, top_down=False)
+            if self.verbose:
+                if self.progress_bar == 'rich':
+                    f_rich_progress.print(metric_results)
+                else:
+                    logger.info(metric_results)
         self.driver.set_model_mode(mode='train')
-        if self.verbose:
-            if self.progress_bar == 'rich':
-                f_rich_progress.print(metric_results)
-            else:
-                logger.info(metric_results)
 
         return metric_results